1,547 research outputs found
Parametric Connectives in Disjunctive Logic Programming
Disjunctive Logic Programming (\DLP) is an advanced formalism for Knowledge
Representation and Reasoning (KRR). \DLP is very expressive in a precise
mathematical sense: it allows to express every property of finite structures
that is decidable in the complexity class \SigmaP{2} (\NP^{\NP}).
Importantly, the \DLP encodings are often simple and natural.
In this paper, we single out some limitations of \DLP for KRR, which cannot
naturally express problems where the size of the disjunction is not known ``a
priori'' (like N-Coloring), but it is part of the input. To overcome these
limitations, we further enhance the knowledge modelling abilities of \DLP, by
extending this language by {\em Parametric Connectives (OR and AND)}. These
connectives allow us to represent compactly the disjunction/conjunction of a
set of atoms having a given property. We formally define the semantics of the
new language, named and we show the usefulness of the
new constructs on relevant knowledge-based problems. We address implementation
issues and discuss related works
Disjunctive Logic Programs with Inheritance
The paper proposes a new knowledge representation language, called DLP<,
which extends disjunctive logic programming (with strong negation) by
inheritance. The addition of inheritance enhances the knowledge modeling
features of the language providing a natural representation of default
reasoning with exceptions.
A declarative model-theoretic semantics of DLP< is provided, which is shown
to generalize the Answer Set Semantics of disjunctive logic programs.
The knowledge modeling features of the language are illustrated by encoding
classical nonmonotonic problems in DLP<.
The complexity of DLP< is analyzed, proving that inheritance does not cause
any computational overhead, as reasoning in DLP< has exactly the same
complexity as reasoning in disjunctive logic programming. This is confirmed by
the existence of an efficient translation from DLP< to plain disjunctive logic
programming. Using this translation, an advanced KR system supporting the DLP<
language has been implemented on top of the DLV system and has subsequently
been integrated into DLV.Comment: 28 pages; will be published in Theory and Practice of Logic
Programmin
Computing Preferred Answer Sets by Meta-Interpretation in Answer Set Programming
Most recently, Answer Set Programming (ASP) is attracting interest as a new
paradigm for problem solving. An important aspect which needs to be supported
is the handling of preferences between rules, for which several approaches have
been presented. In this paper, we consider the problem of implementing
preference handling approaches by means of meta-interpreters in Answer Set
Programming. In particular, we consider the preferred answer set approaches by
Brewka and Eiter, by Delgrande, Schaub and Tompits, and by Wang, Zhou and Lin.
We present suitable meta-interpreters for these semantics using DLV, which is
an efficient engine for ASP. Moreover, we also present a meta-interpreter for
the weakly preferred answer set approach by Brewka and Eiter, which uses the
weak constraint feature of DLV as a tool for expressing and solving an
underlying optimization problem. We also consider advanced meta-interpreters,
which make use of graph-based characterizations and often allow for more
efficient computations. Our approach shows the suitability of ASP in general
and of DLV in particular for fast prototyping. This can be fruitfully exploited
for experimenting with new languages and knowledge-representation formalisms.Comment: 34 pages, appeared as a Technical Report at KBS of the Vienna
University of Technology, see http://www.kr.tuwien.ac.at/research/reports
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